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Cluster analysis aims at classifying data elements into different categories according to their similarity. It is a common task in data mining and useful in various field including pattern recognition, machine learning, information retrieval and so on. As an extensive studied area, many clustering methods are proposed in literature. Among them, some methods are focused on mining clusters with arbitrary...
We introduce a data-driven approach to complete partial 3D shapes through a combination of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input shape, our method first infers a low-resolution – but complete – output. To this end, we introduce a 3D-Encoder-Predictor Network (3D-EPN) which is composed of 3D convolutional layers. The network is...
This paper tackles the problem of reconstructing 3D human poses from 2D landmarks, which is still an ill-posed problem. A widely-used approach is active shape model (ASM) which considers an unknown 3D shape as a linear combination of predefined basis shapes. The existing methods often resolve an optimization problem to reckon the weights and viewpoints of basis shapes, but they could fall into a locally-optimal...
In this paper, we present a new benchmark (Menpo benchmark) for facial landmark localisation and summarise the results of the recent competition, so-called Menpo Challenge, run in conjunction to CVPR 2017. The Menpo benchmark, contrary to the previous benchmarks such as 300-W and 300-VW, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup...
Facial analysis plays very important role in many vision applications, such as authentication and entertainments. The very early works in the 1990s mostly focus on estimating geometric deformations of facial landmarks to address this task. While in the past several years, more and more efforts have been made to directly learn an appearance regression for facial analysis. Though training regressions...
Tertiary protein structure prediction in silico is one of the most challenging problems in Structural Bioinformatics. The challenge arises due to the combinatorial explosion of plausible shapes, where a long amino acid chain ends up in one out of a vast number of three-dimensional conformations. The rules that govern the biological process are partially known, which difficult the development of robust...
A hierarchical algorithm is presented for optimally and automatically combining various option investments to cost-efficiently realize a desired profit-vs.-underlying-price profile (profit profile). The algorithm assumes that a user-defined reference shape is defined and a set of plain vanilla options in which long and short investment positions can be taken are given. Within the presented framework,...
In this paper, we have implemented and tested a system of detection and recognition of road signs. The approach taken in this work consists of two main modules: a sensor module, which is based on color segmentation and shape detection where we converted the images to the HSV color space, then labeled the detected regions and tested for their shape. A recognition module, Template Matching, whose role...
An Image Retrieval (IR) system is used for accessing and retrieving the images from large image database. Content means the image features like color, texture and shape of the image. For Color feature, it is scaling and rotation invariant. It encrypts the color data they are a good component to use under changing lighting conditions. Three color moments are figured per channel (e.g. 6 minutes if the...
Code smells are sub-optimal coding circumstances such as blob classes or spaghetti code - they have received much attention and tooling in recent software engineering research. Higher-up in the abstraction level, architectural smells are problems or sub-optimal architectural patterns or other design-level characteristics. These have received significantly less attention even though they are usually...
In this paper, we address the problem of automated pose classification and segmentation of the left ventricle (LV) in 2D echocardiographic images. For this purpose, we compare two complementary approaches. The first one is based on engineering ad-hoc features according to the traditional machine learning paradigm. Namely, we extract phase features to build an unsupervised LV pose estimator, as well...
Covering the face and all body parts, sometimes the only evidence to identify a person is their hand geometry, and not the whole hand-only two fingers (the index and the middle fingers) while showing the victory sign, as seen in many terrorists videos. This paper investigates for the first time a new way to identify persons, particularly (terrorists) from their victory sign. We have created a new...
Identification and apprehension of criminals by matching facial sketches with photographic faces is one of the major law enforcement applications of the modern world. Majority of the crime occur where there will not be any information available regarding the suspect. In such situation, forensic sketch artist who usually deals with the eyewitness of the crime or victim in order to draw the sketch that...
Recovering depth information from a single still image is an important problem in computer vision. However, the problem is difficult and challenging because it has an infinite number of solutions. To address this issue, humans use numerous visual cues to infer depth. Much progress has been made towards an understanding of the visual mechanisms involved in 3D perception. Such an understanding provides...
We propose a Low-Dimensional Deep Feature based Face Alignment (LDFFA) method to address the problem of face alignment “in-the-wild”. Recently, Deep Bottleneck Features (DBF) has been proposed as an effective channel to represent input with compact, low-dimensional descriptors. The locations of fiducial landmarks of human faces could be effectively represented using low dimensional features due to...
One way to make more spectrum available is through white space sharing, where secondary spectrum users are allowed to transmit from any location except inside exclusion zones (EZs) that are drawn around primary spectrum users (PUs). However, EZ boundaries reveal the locations of PUs, and for some devices, location privacy is extremely important. Location privacy can be improved by increasing the area...
Ear recognition Is still a standing problem In biometrics and has become an open research area in recent years. In this paper, we explore a new local feature extraction technique pyramid histogram of oriented gradients (PHOG) to represent ear images. However, the PHOG descriptor of the ear image is significantly large. To reduce the dimension of the PHOG descriptor, linear discriminant analysis (LDA)...
In this researched paper, a clustering algorithm to discover clusters of unusual shapes and densities. Hierarchical and Density based ways are implemented for constructing minimum Spanning Tree; the MST can be divided into two segments. In the first segment, local density is guesstimate at every data point. In the subsequent segment, hierarchical ways are used by combining clusters according to the...
Person identification from their signatures or verifying the genuineness of official documents like bank cheques, certificates, contract forms, bonds etc. still remains a challenging task when accuracy and computation time are concerned. In this paper, a novel set of features based on the distribution of the quasi-straight line segments has been presented for off-line signature verification. For the...
Pill identification is a serious concern for pharmacists due to similarity of pill appearances. Pill imprints usually contain important information that can be used to add or search for pill information on existing pill databases. However, current techniques for extracting imprints often give results as vectors which cannot be used with existing databases. Thus, this paper proposed an approach for...
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